G-FQZip: Lossless Reference-Based Compression of FASTQ Files Using GPUs

Cong Peng; Qingjin Deng; Zhian Huang; Yiwen Sun; Zexuan Zhu

doi:10.1109/CIS.2017.00128

G-FQZip: Lossless Reference-Based Compression of FASTQ Files Using GPUs

Cong Peng
, Qingjin Deng
, Zhian Huang
, Yiwen Sun^*
, Zexuan Zhu

^*Corresponding author for this work

Shenzhen University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Scopus citations

Abstract

The exponentially increasing high throughput of sequencing data calls for efficient specific compression methods to address the challenges posed by the storage and transmission of such data. In this work, we develop a GPU version of lossless reference-based compression method namely G-FQZip by introducing a GPU-based arithmetic coding, a template matching approach, and a parallel light-weight mapping model. The comparison experiments demonstrate that G-FQZip can improve the (de) compression speed while maintaining comparable compression ratios. Besides, the follow-up evaluation demonstrated the efficiency of the GPU-based arithmetic coding and the template matching approach.

Original language	English
Title of host publication	Proceedings - 13th International Conference on Computational Intelligence and Security, CIS 2017
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	553-556
Number of pages	4
ISBN (Electronic)	9781538648223
DOIs	https://doi.org/10.1109/CIS.2017.00128
State	Published - 2 Jul 2017
Externally published	Yes
Event	13th International Conference on Computational Intelligence and Security, CIS 2017 - Hong Kong, Hong Kong Duration: 15 Dec 2017 → 18 Dec 2017

Publication series

Name	Proceedings - 13th International Conference on Computational Intelligence and Security, CIS 2017
Volume	2018-January

Conference

Conference	13th International Conference on Computational Intelligence and Security, CIS 2017
Country/Territory	Hong Kong
City	Hong Kong
Period	15/12/17 → 18/12/17

Keywords

GPU acceleration
High-throughput sequencing
Lossless compression
Reference-based DNA sequence compression

Access to Document

10.1109/CIS.2017.00128

Cite this

Peng, C., Deng, Q., Huang, Z., Sun, Y., & Zhu, Z. (2017). G-FQZip: Lossless Reference-Based Compression of FASTQ Files Using GPUs. In Proceedings - 13th International Conference on Computational Intelligence and Security, CIS 2017 (pp. 553-556). (Proceedings - 13th International Conference on Computational Intelligence and Security, CIS 2017; Vol. 2018-January). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CIS.2017.00128

Peng, Cong ; Deng, Qingjin ; Huang, Zhian et al. / G-FQZip : Lossless Reference-Based Compression of FASTQ Files Using GPUs. Proceedings - 13th International Conference on Computational Intelligence and Security, CIS 2017. Institute of Electrical and Electronics Engineers Inc., 2017. pp. 553-556 (Proceedings - 13th International Conference on Computational Intelligence and Security, CIS 2017).

@inproceedings{8c289e74c4b7444ea85751305bb21d30,

title = "G-FQZip: Lossless Reference-Based Compression of FASTQ Files Using GPUs",

abstract = "The exponentially increasing high throughput of sequencing data calls for efficient specific compression methods to address the challenges posed by the storage and transmission of such data. In this work, we develop a GPU version of lossless reference-based compression method namely G-FQZip by introducing a GPU-based arithmetic coding, a template matching approach, and a parallel light-weight mapping model. The comparison experiments demonstrate that G-FQZip can improve the (de) compression speed while maintaining comparable compression ratios. Besides, the follow-up evaluation demonstrated the efficiency of the GPU-based arithmetic coding and the template matching approach.",

keywords = "GPU acceleration, High-throughput sequencing, Lossless compression, Reference-based DNA sequence compression",

author = "Cong Peng and Qingjin Deng and Zhian Huang and Yiwen Sun and Zexuan Zhu",

note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 13th International Conference on Computational Intelligence and Security, CIS 2017 ; Conference date: 15-12-2017 Through 18-12-2017",

year = "2017",

month = jul,

day = "2",

doi = "10.1109/CIS.2017.00128",

language = "英语",

series = "Proceedings - 13th International Conference on Computational Intelligence and Security, CIS 2017",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "553--556",

booktitle = "Proceedings - 13th International Conference on Computational Intelligence and Security, CIS 2017",

address = "美国",

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Peng, C, Deng, Q, Huang, Z, Sun, Y & Zhu, Z 2017, G-FQZip: Lossless Reference-Based Compression of FASTQ Files Using GPUs. in Proceedings - 13th International Conference on Computational Intelligence and Security, CIS 2017. Proceedings - 13th International Conference on Computational Intelligence and Security, CIS 2017, vol. 2018-January, Institute of Electrical and Electronics Engineers Inc., pp. 553-556, 13th International Conference on Computational Intelligence and Security, CIS 2017, Hong Kong, Hong Kong, 15/12/17. https://doi.org/10.1109/CIS.2017.00128

G-FQZip: Lossless Reference-Based Compression of FASTQ Files Using GPUs. / Peng, Cong; Deng, Qingjin; Huang, Zhian et al.
Proceedings - 13th International Conference on Computational Intelligence and Security, CIS 2017. Institute of Electrical and Electronics Engineers Inc., 2017. p. 553-556 (Proceedings - 13th International Conference on Computational Intelligence and Security, CIS 2017; Vol. 2018-January).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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KW - Reference-based DNA sequence compression

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Peng C, Deng Q, Huang Z, Sun Y, Zhu Z. G-FQZip: Lossless Reference-Based Compression of FASTQ Files Using GPUs. In Proceedings - 13th International Conference on Computational Intelligence and Security, CIS 2017. Institute of Electrical and Electronics Engineers Inc. 2017. p. 553-556. (Proceedings - 13th International Conference on Computational Intelligence and Security, CIS 2017). doi: 10.1109/CIS.2017.00128

G-FQZip: Lossless Reference-Based Compression of FASTQ Files Using GPUs

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